A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
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ECML-94 Proceedings of the European conference on machine learning on Machine Learning
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
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The Journal of Machine Learning Research
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
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CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Pattern Recognition and Machine Learning (Information Science and Statistics)
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ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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This paper presents an algorithmic framework for feature selection, which selects a subset of features by minimizing the nonparametric Bayes error. A set of existing algorithms as well as new ones can be derived naturally from this framework. For example, we show that the Relief algorithm greedily attempts to minimize the Bayes error estimated by k-Nearest-Neighbor method. This new interpretation not only reveals the secret behind Relief but also offers various opportunities to improve it or to establish new alternatives. In particular, we develop a new feature weighting algorithm, named Parzen-Relief, which minimizes the Bayes error estimated by Parzen method. Additionally, to enhance its ability to handle imbalanced and multiclass data, we integrate the class distribution with the max-margin objective function, leading to a new algorithm, named MAP-Relief. Comparison on benchmark data sets confirms the effectiveness of the proposed algorithms.